#Useful functions for this notebook
#Function to show %
def autopct_format(values):
def my_format(pct):
total = sum(values)
val = int(round(pct*total/100.0))
return '{:.1f}%\n({v:d})'.format(pct, v=val)
return my_format
# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
"""
Barplot with percentage at the top
data: dataframe
feature: dataframe column
perc: whether to display percentages instead of count (default is False)
n: displays the top n category levels (default is None, i.e., display all levels)
"""
total = len(data[feature]) # length of the column
count = data[feature].nunique()
if n is None:
plt.figure(figsize=(count + 2, 6))
else:
plt.figure(figsize=(n + 2, 6))
plt.xticks(rotation=90, fontsize=15)
ax = sns.countplot(
data=data,
x=feature,
palette="Paired",
order=data[feature].value_counts().index[:n],
)
for p in ax.patches:
if perc == True:
label = "{:.1f}%".format(
100 * p.get_height() / total
) # percentage of each class of the category
else:
label = p.get_height() # count of each level of the category
x = p.get_x() + p.get_width() / 2 # width of the plot
y = p.get_height() # height of the plot
ax.annotate(
label,
(x, y),
ha="center",
va="center",
size=12,
xytext=(0, 5),
textcoords="offset points",
) # annotate the percentage
plt.show() # show the plot
#function to create histograms and boxplots at once
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
"""
Boxplot and histogram combined
data: dataframe
feature: dataframe column
figsize: size of figure (default (15,10))
kde: whether to show the density curve (default False)
bins: number of bins for histogram (default None)
"""
f2, (ax_box2, ax_hist2) = plt.subplots(
nrows=2, # Number of rows of the subplot grid= 2
sharex=True, # x-axis will be shared among all subplots
gridspec_kw={"height_ratios": (0.25, 0.75)},
figsize=figsize,
) # creating the 2 subplots
sns.boxplot(
data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
) # boxplot will be created and a triangle will indicate the mean value of the column
sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
) if bins else sns.histplot(
data=data, x=feature, kde=kde, ax=ax_hist2
) # For histogram
ax_hist2.axvline(
data[feature].mean(), color="green", linestyle="--"
) # Add mean to the histogram
ax_hist2.axvline(
data[feature].median(), color="black", linestyle="-"
) # Add median to the histogram
# function to plot stacked bar chart
def stacked_barplot(data, predictor, target):
"""
Print the category counts and plot a stacked bar chart
data: dataframe
predictor: independent variable
target: target variable
"""
count = data[predictor].nunique()
sorter = data[target].value_counts().index[-1]
tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
by=sorter, ascending=False
)
print(tab1)
print("-" * 120)
tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
by=sorter, ascending=False
)
tab.plot(kind="bar", stacked=True, figsize=(count + 1, 5))
plt.legend(
loc="lower left",
frameon=False,
)
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
plt.show()
#Function to visualize the clusters using pca
def visualize_clusters(data, number_components, number_clusters):
"""
data: dataframe
number_components: the number of components to perform principal component analysis
number of clusters: how many clusters are visualized
"""
#set data to number_dimensions
pca=PCA(number_components)
#transform the data
pca_df = pca.fit_transform(data)
#redo kmeans
kmeans_pca = KMeans(n_clusters=number_clusters)
#predict labels
label = kmeans_pca.fit_predict(pca_df)
#Get unique labels
u_labels = np.unique(label)
#Getting the Centroids
centroids = kmeans_pca.cluster_centers_
#plotting the results:
titleLabel = "Cluster Plot Kn = "+ str(number_clusters)
plt.title(titleLabel)
for i in u_labels:
plt.scatter(pca_df[label == i , 0] , pca_df[label == i , 1] , label = i)
plt.scatter(centroids[:,0] , centroids[:,1] , s = 80, color = 'k')
plt.legend()
plt.show()
#Function to visualize the clusters using pca for hc
def visualize_hc_clusters(data, number_components, number_clusters):
"""
data: dataframe
number_components: the number of components to perform principal component analysis
number of clusters: how many clusters are visualized
"""
#set data to number_dimensions
pca=PCA(number_components)
#transform the data
pca_df = pca.fit_transform(data)
#redo hca
hca_pca = AgglomerativeClustering(n_clusters=number_clusters, affinity="euclidean", linkage="average")
hca_pca.fit(ALB_scaled_df)
#predict labels
label = hca_pca.fit_predict(pca_df)
#Get unique labels
u_labels = np.unique(label)
#plotting the results:
titleLabel = "Cluster Plot Hierachical Clustering number of clusters = "+ str(number_clusters)
plt.title(titleLabel)
for i in u_labels:
plt.scatter(pca_df[label == i , 0] , pca_df[label == i , 1] , label = i)
plt.legend()
plt.show()
#function to plot dendrogram
def plotDendrogram(*Z,leafrotation,colorthreshold,leafontsize):
"""
*Z: array
leafrotation: vertical or horizontal
colorthreshold: colors
leafontsize: size of font
"""
plt.figure(figsize=(10, 5))
plt.title('Agglomerative Hierarchical Clustering Dendogram')
plt.xlabel('sample index')
plt.ylabel('Distance')
dendrogram(Z, leafrotation, colorthreshold, leafontsize)
plt.tight_layout()
def dendrogram_single_plot(data, d_method, d_metric, y_dist):
plt.figure(figsize=(10, 7))
title_label = "Dendrogram for method: " + d_method + " metric: "+d_metric +" distance : "+ str(y_dist)
plt.title(title_label)
# Selecting method and metric by index
clusters = shc.linkage(data,
method=d_method,
metric=d_metric)
shc.dendrogram(Z=clusters)
plt.axhline(y = y_dist, color = 'black', linestyle = '-')
AI Generated image.
